where \(\theta _c\)  are the learnable parameters, and \(K\)  is the number of clusters. We find the similarity between embedded point \(z_i = f(x_i)\)  and the latent cluster with centroid \(\mu _k\) , \(k \in {1, \dots , K}\) , using the student's t-distribution [1]} with one degree of freedom and normalized over all clusters \(K\) :
\(q_{ik}=\frac{\left[1+\sum _{j=1}^J (z_{i,j} - \mu _{k,j})^2\right]^{-1}}{\sum _{k=1}^K\left[1+\sum _{j=1}^J (z_{i,j} - \mu _{k,j})^2\right]^{-1}}\) 
